Method of sensing traffic and detecting traffic situations on roads, preferably freeways

ABSTRACT

A method of sensing traffic and detecting traffic situations on roads (AS), preferably freeways. With measuring points (measuring cross sections MQ1, MQ2, . . . ) set up for the purpose for vehicle detection using traffic sensors (VS) and with a traffic data processing arrangement (VDVE) for traffic control, at regular intervals traffic data (VD), such as vehicle speed (v), traffic intensity (Q) and traffic density (K), are determined and traffic parameters determined therefrom are formed in a traffic data processing system (VDA). Two adjacent measuring points. (MQi, MQ(i+1) form a measuring section (MA) of a given length (l). The following traffic parameters are formed from the traffic data (VD) of two such measuring points: 
     a) the speed density difference (vk-D), which is calculated from the local traffic data of average speed (v) and traffic density (K); 
     b) a trend factor (FT), which is formed continually from the ratio between the traffic intensities (Qi/Q(i+1)) of the first and second measuring points (MQi, MQ(i+1)), but determined during a given period (t) in the minute range; 
     c) the traffic intensity trend (QTi, QT(i+1)) of the respective measuring point (MQi, MQ(i+1)), the trend being derived on the basis of the function of the traffic intensity (Q) over the time (curve Q (t)) from the increase of the tangent to the curve. The probability of a critical traffic situation (WG) is derived therefrom in a fuzzy logic (FUB).

BACKGROUND OF THE INVENTION

The invention relates to a method of sensing traffic and detectingtraffic situations on roads, the roads having measuring points andmeasuring cross sections.

The constantly increasing volume of traffic on the roads, in particularfreeways, with the resulting reduction in safety and the difficulties incorrespondingly expanding the overall road or freeway network have ledin recent decades to considerations concerning the use of electronicsfor increasing the efficiency and safety of roads.

There are in the meantime various systems and various methods which, onthe basis of traffic measurements, switch appropriate displays on thevariable message signs. The control is point-specific, if it relates toa specific point of the traffic flow (for example roadworks or narrowingof roadways), section-specific (generally referred to by the term "lineinfluencing"), if its relates to a section, or network-specific, if itundertakes the automatic diversion from a normal route to an alternateroute (variable route guidance).

Existing line influencing systems are very complex and expensive and aretherefore installed selectively on sections where there is particularlyheavy traffic. At the same time they require very high expenditure withrespect to data acquisition and evaluation and for informationtransmission by means of variable message signs. To maintain clearrelationships between the traffic situation and the controlled display,the control logic is of a relatively simple construction. The processedlocal measured values, such as generally smoothed traffic intensity,smoothed speed and local traffic density, are compared with predefinedthreshold values in order to make a statement or to activate thevariable message sign.

In the case of systems in operation with traffic sensing and control ofthe traffic by variable message signs, until now the control has beencarried out by definitive Yes/No statements, based on decision logics.For example, if traffic is heavy it can be harmonized by displaying thesame speed restriction on all lanes. If the driving speed is reduced, ajam can be detected and a warning given. If relatively great trafficdisruption is detected, it can be counteracted by means of a uniformspeed restriction. Weather-dependent ambient conditions, which aresensed by separate sensors, are likewise displayed for line influencing.An early and reliable detection of hazardous traffic conditions is notreadily possible with the known systems, because the traffic dataacquired do not provide clear information on the actual trafficsituation.

European reference EP-A-0 171 098 discloses a method of sensing trafficand controlling traffic on roads which has at least two measuring pointsfor vehicle detection with traffic sensors. In this method, traffic datain the form of vehicle speeds are determined, processed and assessedtaking into account the traffic intensity. It involves considering thedetermined speed data of at least two measuring points spaced apart by acertain length and comparing them with predetermined speed values on thebasis of logic decisions.

The article "Traffic Prediction Method by Fuzzy Logic", Second IEEEInternational Conference on Fuzzy Systems (Cat. No. 93CH3136-9),Proceedings of IEEE 2nd International Fuzzy Systems Conference, SanFrancisco, Calif., U.S.A., 28 Mar.-1 Apr. 1993, ISBN 0-7803-0614-7,1993, New York, N.Y., U.S.A., IEEE, U.S.A., pages 673-678 Vol. 2, IokibeT. et al., discloses a method in which only the traffic intensity ismeasured and this is assessed together with empirical traffic intensityvalues with the aid of fuzzy logic in order to obtain an estimate of thetraffic to be expected.

SUMMARY OF THE INVENTION

The object of the invention is an early and reliable automatic detectionof critical traffic situations, such as traffic disruptions caused bythe forming of a jam or an accident, on roads in order to warn the roadusers of this situation in good time.

In general terms the present invention is a method for sensing trafficand detecting traffic situations on roads, preferably freeways. Thewords have measuring points, referred to as measuring cross sections,for vehicle detection using traffic sensors and a traffic dataprocessing arrangement for traffic control. At regular intervals trafficdata, such as vehicle speed, traffic intensity, this is the number ofvehicles at a measuring cross section based on a unit of time, andtraffic density, that is the number of vehicles based on a specificsection of road, is determined at the measuring points. Trafficparameters are determined therefrom and are formed in a traffic dataprocessing system. First and second adjacent measuring points form ameasuring section of a given length of road.

The following traffic parameters are formed from the traffic data of thefirst and second adjacent measuring points.

A speed density difference is calculated according to the followingrelationship: ##EQU1## where vfi, vf(i+1) are adjustable maximum valuesof vehicle speed at the first and second measuring points, respectively;

kmaxi,

kmax (i+1) are adjustable maximum values of the traffic density at thefirst and second measuring points, respectively;

ki is the traffic density after the first measuring point;

k(i+1) is the traffic density before the second measuring points;

vi, v(i+1) are average speeds at the first and second measuring points,respectively; and

vk-D is the speed density difference.

A trend factor is formed continually from the ratio between the trafficintensities of the first and second measuring points, but determinedduring a given period in the minute range. Traffic intensity trends ofthe respective first and second measuring points are derived on thebasis of the function of the traffic intensity over the time from anincrease of the tangent to the curve. These three traffic parameters areprocessed with fuzzy logic for the detection of a critical trafficsituation in the measuring section. They are fed as probabilityvariables to a downstream result assessment arrangement, in whichcontrol signals for variable message signs are formed in dependence onadjustable threshold values.

Advantageous developments of the present invention are as follows.

The traffic parameters of speed density difference and trend factor aredynamically calibrated in dependence on their past values. A calibrationfactor for the speed density difference and a calibration factor for thetrend factor are formed from the traffic data. In a calibrationarrangement, arranged between the traffic data processing system and thefuzzy processing, the current speed density difference is divided by thespeed density difference calibration factor and the respective currenttrend factor is divided by the trend factor calibration factor.

For calibration of the speed density difference, the latter is assessed.The value of the calibration factor for the speed density difference isa threshold value for the speed density difference from which there is ahigh probability of a critical traffic situation.

For calibration of the trend factor, a characteristic value of the trendfactor estimated to be "small" is defined such that it comprises the setof the values of the trend factor whose relative cumulative frequencylies below a threshold value. A frequency table is formed with aplurality of classes with defined ranges of values of the trend factor.The current trend factor is assigned to a class in order to determinethe calibration factor from it.

A disruption is determined and displayed as a critical trafficsituation. The trend factor and the traffic intensity trend of the firstmeasuring point are used to detect bunching and to form a bunchingprobability variable (PWG), the relationship of which with the trafficintensity trend of the second measuring point is established in order toderive a disruption criterion. The trend factor and the speed densitydifference and also the disruption criterion are also used to detect adisruption and to form a disruption probability variable.

With the method according to the invention, traffic data are acquired onthe roads by measuring points set up for the purpose, that is to sayrespective measuring cross sections with traffic sensors fitted for eachlane, and are processed in a processing arrangement provided for thepurpose for a traffic control means. Specific traffic parameters arederived in a traffic data processing arrangement from the regularlyacquired traffic data: speed and traffic intensity. For this purpose,two adjacent measuring points form a measuring section, which is of agiven length, for example 3 km. The following traffic parameters areformed from the traffic data from these measuring points:

A speed density difference (vk-D) according to the relationshipdescribed above. The speed density difference takes into account thespeed and the traffic density of both measuring cross sections. As asecond traffic parameter, a trend factor is formed, which is formedcontinually from the ratio between the traffic intensities of the firstand second measuring points, but only the values during a given period,for example the last 30 minutes, are taken into account. As the thirdtraffic parameter, the traffic intensity trend of the respectivemeasuring point is formed as a measure of the dynamic situationdevelopment, ie. the development over time of the traffic intensity. Inthis case, the trend of the traffic intensity is derived from thefunction of the traffic intensity over time or from the increase of thetangent to this function curve. These three traffic parameters areprocessed in a fuzzy logic to detect critical traffic situations, inorder to obtain as an output variable a statement on the probability ofa critical traffic situation. This probability variable is assessed independence on a predeterminable threshold value in order to generate adisplay recommendation for the variable message signs.

The use of fuzzy logic for detecting the traffic situation on roads hasa series of advantages. The evaluating of the input data is very simple.A plurality of inputs can be further combined. As a result, it ispossible to use a plurality of inputs simultaneously for one measure,even if they are not particularly meaningful individually. This leads onaverage to a faster response time. In addition, more complicated logicsfor situation interpretation, which are possible only by the combinationof many data items (traffic intensity, speed and local density at thecross section and at the preceding or subsequent measuring crosssection, trend factors, possibly ambient data) can be managed betterwith fuzzy logic. By virtue of the flexible thought processes of fuzzylogic, instead of a rigid binary decision (jam or no jam at a crosssection) it is possible to determine a smooth transition, which may berepresented in the form of a probability (for example the probability ofa jam at this cross section is 70%). This has the advantage that thisresult can be assessed with a correspondingly predeterminable thresholdvalue such that a reliable display recommendation can be announced at anearly time.

Apart from the vehicle speed, which is determined at both measuringpoints and is generally processed as a smoothed mean value (v) for therespective measuring point, the traffic intensity (Q), which is alsoreferred to as volume of traffic, and the traffic density (K) are usedas traffic data. The traffic intensity indicates the number of vehiclesat a measuring cross section, based on a unit of time, for example onehour. The traffic density is a measure of the number of vehicles basedon a specific section of road. Use is made of a so-called local trafficdensity, which relates the number of vehicles to the measuring crosssection and takes into account the corresponding speed. The trafficdensity is the quotient of the traffic intensity and the average speed(K=Q/v).

The traffic parameter of speed density difference vk-D is calculatedfrom the local traffic data on average speed and the traffic density oftwo adjacent measuring cross sections (measuring points) according tothe formula described above. The first term of the speed densitydifference relates to the measuring cross section MQi, the second to thedownstream measuring cross section MQ(i+1). In order to be able tocompare the traffic variables of different measuring cross sections,they are respectively referred to the adjustable maximum values of thetraffic variables of the cross sections (max. free speed and max.traffic density). If traffic conditions at the measuring cross sectionare undisturbed, ie. the speed is not low and the traffic density is nothigh, the corresponding term moves in the range of very low values. Ifunstable traffic conditions prevail at the measuring cross section, ie.the speed is low and the traffic density is high, the value of the termconcerned increases. Conclusions as to the current traffic conditionscan thus be drawn from the difference between the two terms.

The trend factor (FT) is used as an indicator of a disruption. It isused to monitor the flow of vehicles into and out of the measuringsection (MA), which may be of a given length, for example 3 km, and isformed by the two measuring points (MQi and (MQi+1)). In the case of acritical traffic situation, more vehicles enter the measuring sectionthan leave it, as a result the trend factor (FT) increasesexponentially. The calculation of the trend factor is based on thegenerally unsmoothed volumes of traffic, ie. the traffic intensities atthe two measuring sections. Consequently, a higher accuracy and a fasterresponse are achieved. In order to reduce the influence of measuringerrors, the trend factor is respectively calculated only on the basis ofthe last measuring intervals, which means a period of, for example, 30minutes.

The third traffic parameter, the traffic intensity trend (QTi), servesfor assessment of the dynamic situation development. The calculation isbased on the generally unsmoothed acquired traffic data. The trafficintensity trend is likewise considered at the two measuring crosssections.

These three traffic parameters are the input data for the fuzzy logic.The latter establishes a relationship between the input variables, whichoriginate from two adjacent measuring cross sections, by means of aknowledge base defined by rules and derives from this relationship theprobability of a critical traffic situation, for example a disruption.

The input variables of the fuzzy logic are dependent on many influences,in particular on the distance between the measuring points, the geometryof the section of road, ie. incline or decline, ambient conditions suchas wetness, snow, black ice, day or night, and possible furtherinfluences. The influences are thus not only of a steady state, but alsoof a dynamic type. Therefore, in a development of the method accordingto the invention, the traffic parameters are calibrated such that thefuzzy system can always assess the input variables (traffic parameters)in the same way irrespective of external influences. For this purpose,the variables are dynamically calibrated in dependence on their pastvalues.

In order to minimize the expenditure for the calibration for identifyinga critical traffic situation, the trend factor and the speed differenceare automatically calibrated. For this purpose, a calibration factor forthe speed density difference and a calibration factor for the trendfactor are formed from these traffic data and their relationship withthe current traffic parameters is established in a calibrationarrangement, which is arranged between the traffic data processing andthe fuzzy processing. The current speed density difference is divided bythe speed density difference calibration factor and the respectivecurrent trend factor is divided by the trend calibration factor.

As already stated, the speed density difference is dependent on ambientconditions, such as wetness, fog, day/night etc. This fuzzy inputvariable is therefore assessed by the dynamic calibration factor. Thevalue of this factor may apply as a threshold for the speed densitydifference, from which there is a high probability of the case of acritical traffic situation (disruption) existing. The calibration factoris calculated only if the speed density difference lies below a specificthreshold, for example 0.3. The factor is made up of the mean value, thestandard deviation from the speed density difference and its fixedthreshold. The calculation of the mean value and of the standarddeviation is carried out only on the basis of the relevant maxima of thespeed density difference profile:

    vk.sub.-- Diff.sub.mittel =α·vk.sub.-- Diff+(1-α)·vk.sub.-- Diff.sub.mittelalt

    σ.sub.vk.sbsb.--.sub.Diff =α·(vk.sub.-- Diff-vk.sub.-- Diff.sub.mittel).sup.2 +(1-α)·σ.sub.vk.sbsb.--.sub.Diffalt

    vk.sub.-- Diff.sub.mittelalt =vk.sub.-- Diff.sub.mittel

    σ.sub.vk.sbsb.--.sup.Diffalt =σ.sub.vk.sbsb.--.sub.Diff ##EQU2## where α=0.05 (adjustable) PSG=positive very big

The current speed density difference is divided by this calibrationfactor. For the calibration of the trend factor, the characteristicvalue of the trend factor is sought and is estimated to be "small". Thischaracteristic valve is defined such that it comprises the set of allthe values of the trend factor whose relative cumulative frequency liesbelow a threshold value. For this purpose, a frequency table isintroduced, the classes of which are defined according to the table. Aclass is a defined range of values of the trend factor, all the classestogether describing the total range of values of the trend factor. Foreach measuring interval, the current trend factor is assigned to aclass; the respective class is then incremented. For each interval,consequently, the measured value for which the relative cumulativefrequency lies below the predetermined threshold value is determined.

The classification is broader for very small and large values; a finerclassification is chosen for the important calibration range:

    __________________________________________________________________________    Class   0     1   2   3   . . .                                                                           35  36  37                                        __________________________________________________________________________    Range of charac-                                                                      -1 . . . 0.15                                                                       >0.15                                                                             >0.225                                                                            >0.275                                                                            . . .                                                                           >1.925                                                                            >1.975                                                                            >2.025                                    teristic values                                                               F.sub.T       ≦0.225                                                                     ≦0.275                                                                     ≦0.325                                                                       ≦1.975                                                                     ≦2.025                                                                     ≦2.075                             Classification                                                                __________________________________________________________________________

The calibration factor is then calculated as follows: ##EQU3##

The current trend factor is respectively divided by this calibrationfactor.

The method according to the invention of detecting critical trafficsituations is used in a special development of the invention fordetecting a disruption. In this case, bunching is detected in a bunchingdetection from the traffic parameters: trend factor and trafficintensity trend of the first measuring cross section, and a bunchingprobability variable is derived. In a preliminary disruptioninvestigation, a disruption criterion is derived with the aid of thefuzzy decision from the traffic parameter of traffic intensity trend ofthe second measuring cross section and the bunching probability variableand, considered together with the trend factor and the speed densitydifference, makes detection of a disruption possible. Apart from thedecision criteria of speed density difference and trend factor, thetraffic parameters of traffic intensity trend at the measuring point MQ1and at the measuring point MQ2, with which a preliminary investigationfor a disruption is carried out, are used for the fuzzy disruptiondecision.

A bunching detection is carried out. Bunching is a group of vehicles ofhigh traffic intensity and traffic density entering the measuringsection.

The trend factor traffic parameter used for disruption detection allowstwo interpretation possibilities in cases of very great values. There isa disruption, ie. during quite a long period more vehicles have enteredthe measuring section than have left it, or a bunch has entered themeasuring section. A bunch is to some extent a density wave, as occursfor example when a bottleneck is suddenly removed. To be able todistinguish reliably between these two cases, as stated above, bunchingdetection is carried out. The traffic intensity trend, the bunchingprobability in the preceding measuring interval, and the trend factorare used as the input variable of the fuzzy logic. Available directly asthe output variable is a value for the probability of bunching in themeasuring section being considered.

With the preliminary disruption investigation, the possibility of adisruption is concluded from the variables of traffic intensity trend,old disruption probability and bunching probability . The possibility ofa disruption is represented by the disruption criterion output variable.If this value is high, the preliminary investigation indicates adisruption.

If, then, in addition the traffic intensity trend at the downstreammeasuring cross section decreases at a high value for a disruptioncriterion, the probability of a disruption is very high. With anincreasing traffic intensity trend at the downstream measuring crosssection, the possibility of a disruption drops, as it does in the caseof an increase in the bunching probability. The case in which adisruption was very probable in the last measuring interval constitutesan exception. In this case, the disruption criterion is substantiallyindependent of the bunching probability and the traffic intensity trend,since in the case of the disruption already detected in the lastmeasuring interval both the bunching probability and the trafficintensity trend may further increase. The disruption detection is thedecision stage which ultimately leads to the result of the probabilityof a disruption. In dependence on this variable, a warning is sent tothe display cross section.

As already explained, the disruption probability is derived by means ofa fuzzy control base from the variables: disruption criterion, trendfactor and speed density difference. In the case of a very greatpositive speed density difference, a disruption very probably exists.The greater (positively) the speed density difference, the more probablea disruption. With a rising trend factor, if the speed densitydifference is positive, the probability of a disruption increases evenmore. If the disruption criterion is great, the trend factor has moreinfluence. If the disruption criterion is smaller, ie. thecharacteristics do not indicate a disruption, the speed densitydifference alone decides, since it is in this case more reliable thanthe trend factor. In the result assessment, on the basis of theprobability of a disruption, a display recommendation, for example jamwarning, is derived for the variable message signs and the display isinitiated.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention which are believed to be novel,are set forth with particularity in the appended claims. The invention,together with further objects and advantages, may best be understood byreference to the following description taken in conjunction with theaccompanying drawings, in the several Figures of which like referencenumerals identify like elements, and in which:

FIG. 1 shows a basic representation of the method according to theinvention,

FIG. 2 shows a basic representation of a calibration and

FIG. 3 shows a basic representation of a disruption detection.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The road AS, shown here as a freeway with, for example, two lanes in onetravelling direction, has two measuring cross sections MQi and MQ(i+1),which are arranged at a specific distance and form a measuring sectionMA. With the traffic sensors VS, for example vehicle detectors, whichmay be formed for example by double induction loops, traffic data VD areacquired and fed to a traffic data processing system VDA. The speed v,the traffic density K and the traffic intensity Q are acquired astraffic data and are further processed. In the traffic data processingsystem VDA, the traffic parameters: speed density difference vk-D, thetrend factor FT and the traffic intensities QTi and QTi+1 are determinedseparately at the measuring cross sections MQi and MQi+t and are fed toa fuzzy logic for further processing. The fuzzy processing arrangementis denoted by FUB. The probability variable WG for a critical trafficsituation which is formed there, as already explained above, is assessedin the result assessment arrangement EBE on the basis of apredeterminable threshold value SW in order to generate a control signalSG, for example as a display recommendation, for a variable message signVWZ.

In FIG. 2, the calibration already described above is schematicallyrepresented. In an arrangement for calibration factor formation KFB, thetraffic data VD or traffic parameters vk-D and FT are used for forming acalibration factor for the speed density difference KFv and acalibration factor for the trend factor KFT. These factors are fed tothe calibration arrangement KE, in which the traffic parameters of speeddensity difference and trend factor are consequently calibrated and fedas calibrated parameters vk-D; FT to the fuzzy processing FUB for thealready explained further processing.

In FIG. 3, the disruption detection is schematically represented. In thebunching detection PE, a bunching probability variable PWG is derivedwith the aid of the fuzzy logic from the input variables of trend factorFT and traffic intensity trend QTi at the measuring cross section MQi.This bunching probability variable PWG is considered in a preliminarydisruption investigation STV with the traffic variable of trafficintensity trend QT(i+1) of the measuring cross section MQ(i+1) and adisruption criterion STK is derived from it. This criterion STK isconsidered together with the trend factor FT and the speed densitydifference vk-D in order to be able to conclude whether there is adisruption. This is indicated by the disruption detection STE. Asexplained above, in the disruption detection STE a disruptionprobability variable SWG is concluded and is treated further in asubsequent result assessment arrangement EBE.

The invention is not limited to the particular details of the methoddepicted and other modifications and applications are contemplated.Certain other changes may be made in the above described method withoutdeparting from the true spirit and scope of the invention hereininvolved. It is intended, therefore, that the subject matter in theabove depiction shall be interpreted as illustrative and not in alimiting sense.

What is claimed is:
 1. A method for sensing traffic and detectingtraffic situations on roads having measuring points set up as measuringcross sections for vehicle detection using traffic sensors and a trafficdata processing arrangement for traffic control, comprising the stepsof:determining at regular intervals traffic data, said traffic data haveat least vehicle speed, traffic intensity, which is the number ofvehicles at a measuring cross section based on a unit of time, andtraffic density which is to say the number of vehicles based on aspecific section of road, at the measuring points; forming in a trafficdata processing system traffic parameters determined from the trafficdata and the traffic density, first and second adjacent measuring pointsfurthermore forming a measuring section of a given length; forming aspeed density difference according to the following relationship:##EQU4## where vfi, vf(i+1) are adjustable maximum values of which speedat the first and second measuring points, respectively; kmaxi, kmax(i+1)are adjustable maximum values of traffic density at the first and secondmeasuring points, respectively; ki is traffic density after the firstmeasuring point; k(i+1) is traffic density before the second measuringpoint; vi, v(i+1) are average speeds at the first and second measuringpoints, respectively; vk-D is the speed density difference;the speeddensity difference, the trend factor and the traffic intensity trendsbeing traffic parameters; forming a trend factor continually from aratio between traffic intensities of the first and second measuringpoints, but determined during a given time period in a minute range; theforming a traffic intensity trend of a respective measuring point of thefirst and second measuring points, the trend being derived by a functionof the traffic intensity over time from an increase of tangent to acurve of the function, traffic parameters with fuzzy logic forprocessing a direction of a critical traffic situation in the measuringsection and feeding the traffic parameters as probability variables to adownstream result assessment arrangement, in which control signals forvariable message signs are formed in dependence on adjustable thresholdvalues.
 2. The method as claimed in claim 1, wherein the trafficparameters of speed density difference and trend factor are dynamicallycalibrated in dependence on their past values, calibration factor forthe speed density difference and a calibration factor for the trendfactor being formed from the traffic data, and wherein in a calibrationarrangement, arranged between the traffic data processing system and thefuzzy processing, a current speed density difference is divided by thespeed density difference calibration factor and the current trend factoris divided by the trend factor calibration factor.
 3. The method asclaimed in claim 2, wherein, for calibration of the speed densitydifference, the speed density difference (latter) is assessed, a valueof the calibration factor for the speed density difference being athreshold value for the speed density difference from which there is ahigh probability of a critical traffic situation.
 4. The method asclaimed in claim 2, wherein, for calibration of the trend factor, acharacteristic value of the trend factor estimated to be "small" isdefined such that characteristic value comprises a set of all values ofthe trend factor whose relative cumulative frequency lies below athreshold value, a frequency table being formed with a plurality ofclasses with defined ranges of values of the trend factor, and thecurrent trend factor being assigned to a class in order to determine thecalibration factor.
 5. The method as claimed in claim 1, wherein adisruption is determined and displayed as a critical traffic situation,the trend factor and the traffic intensity trend of the first measuringpoint being used to detect bunching and to form a bunching probabilityvariable, a relationship of which with the traffic intensity trend ofthe second measuring point is established in order to derive adisruption criterion, the trend factor and the speed density differenceand the disruption criterion also being used to detect a disruption andto form a disruption probability variable.